Transcranial magnetic stimulation (TMS) is a noninvasive technique used for neuroscience research and treatment of psychiatric and neurological disorders. During TMS, a current-carrying coil placed on the scalp induces an electric field that modulates targeted neuronal circuits. Computational simulations of the electric field (E-field) induced by TMS are increasingly used to gain a mechanistic understanding of the effect of TMS on the brain and to inform its administration. To ensure safe and effective use of computational simulation results, it is of primary importance to systematically quantify and enhance the level of confidence in them. As we show, much of the error inherent to the computational methods deployed for TMS simulation can be controlled by increasing the fidelity of the numerical approximations. However, the accuracy and precision of TMS simulations are still largely uncertain because of inherent variability in TMS setups (e.g. inter-session variability in coil placement and inter-individual differences) and error introduced in the generation of input simulation parameters from experimental data (e.g. error in coil placement measurements and error introduced in an individual head image segmentation process). Finally, there are no existing frameworks that consider this variability in selecting the placement of the TMS coil for most efficient and reliable delivery of E-field to the target. The objective of this project is to develop an uncertainty quantification (UQ) framework for systematically modeling input uncertainties of TMS procedures, quantifying confidence and statistics of TMS simulations, and informing TMS dosimetry. Aim 1 concerns the creation of efficient computational frameworks for rapid and accurate simulation of TMS E-fields. Aim 2 involves the development of UQ methods for analyzing uncertainty and variability in TMS E-field dose. Aim 3 addresses the development of a framework for determining TMS coil placement that maximizes the E- field delivered at the target and minimizes its variability. The proposed work will increase the fidelity and reliability of computational TMS dosimetry and enable more accurate and precise targeting. This could empower TMS researchers and clinicians to quantify statistically the E-field dose, infer sources of variation in experimental and clinical outcomes, and select coil placements that result in increased and consistent efficacy.